In order to detect a specific detection target object in an image without being affected by variations in appearance on the image such as a posture or a shape, there is proposed a method of learning image pattern of the detection target object consisting of a set of local features which are partial images around feature points extracted in the training sample images of the detection target object.
When learning the image pattern of the detection target object from the training sample images, relationships of parameters, such as a position, a scale, or an orientation of the detection target object, relative to the local features are stored in addition to the local features extracted from the partial images of the detection target object.
When detecting the detection target object, detection of the object and estimation of the parameters thereof are performed by matching respective local features extracted from the input images in the same manner as the time of training with the respective learned local features stored at the time of training, selecting the similar learned local features, performing voting in a parameter space such as a type, the position, the scale, or the orientation of the detection target object using the stored relative parameters, and obtaining local maxima at which voting values are sufficiently large.
In B. Leibe, A. Leonardis, B. Schiele, “Robust Object Detection with Interleaved Categorization and Segmentation”, International Journal of Computer Vision, Vol. 77, No. 1-3, pp. 259-289, 2008, the object is detected by matching local features extracted from the input image with representative local features stored at the time of training, selecting the similar ones, and performing the voting in the parameter space of the position and the scale of the detection target object.
In JP-A-2006-65399, the matching of the local features is speeded-up by KD trees. Also, position and posture parameters calculated from a set of features are voted in the parameter space to obtain the position and posture of the detection target object.
In the related art, since the local features are not discriminated whether they are a part of the detection target object or not, the voting is performed from the local features which do not belong to the detection target object, and hence there arises a problem of lowering of detection accuracy.
In view of solving the problems descried above, it is an object of the invention to provide an object detecting apparatus which is able to detect a detection target object with a high degree of accuracy, a method and a program thereof, a learning apparatus therefor, and a method and a program therefor.